HDRNet: Highlight-Corrected Dual-Domain Ratio Network for Low-Light Scene Understanding

Low-light vision remains a fundamental challenge, as illumination degradation impairs downstream tasks. Recent methods build illumination-invariant color ratios in the log domain under a Lambertian model, and Phong-inspired extensions suppress highlight residuals after ratio formation. However, local highlights introduce channel-dependent biases into the log image before ratio construction. Such biases survive cross-channel subtraction and corrupt the ratio maps in the object regions. We propose HDRNet, a compact plug-and-play highlight-corrected dual-domain ratio network for low-light scene understanding. Local Highlight Attenuation module corrects for highlight-induced log-domain bias by predicting a bounded, channel-wise attenuation map from RGB cues and subtracting it before ratio construction, reducing contamination in the resulting ratio features. The corrected signal feeds a dual-branch module for spatial and spectral feature extraction, and a residual gated fusion integrates both branches with RGB appearance cues. Highlight Prior Regularization supervises the front-end during training. HDRNet delivers mAP gains of 2.2 points on ExDark and 2.6 points on DarkFace, and achieves 42.0% mask AP on LIS and 62.1% mIoU on ACDC-Night. Controlled ratio-recovery experiments support the correction mechanism, while highlight-stratified evaluation shows the largest detection gains under strong background highlights. Code will be released upon acceptance.